scholar.google.com › citations
Aug 3, 2024 · This paper conducts a comprehensive investigation and proposes a novel similarity constraint leveraging non-negative matrix factorization techniques.
To address this gap, this paper conducts a comprehensive investigation and proposes a novel similarity constraint leveraging non-negative matrix factorization ...
Multi-label feature selection via similarity constraints with non-negative matrix factorization. Document Description: SCNMF.m: Implementation function of the ...
Multi-label feature selection via similarity constraints with non ... - OUCI
ouci.dntb.gov.ua › works
Multi-label feature selection via similarity constraints with non-negative matrix factorization · List of references · Publications that cite this publication.
People also ask
What is a non-negative matrix factorization?
What is the key advantage of using non-negative matrix factorization (NMF) for dimensionality reduction?
What is the intuitive of non-negative matrix factorization?
In this paper, we propose a CMFS (Correlated- and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously ...
Feature selection, a meaningful preprocessing technique in machine learning, plays a key role in multi-label learning to select more discriminative features ...
Pub Date: 2024-07-20. Multi-label feature selection via similarity constraints with non-negative matrix factorization · Knowledge-Based SystemsPub Date: 2024 ...
Mar 1, 2024 · A novel embedded multi-label feature selection method, termed global redundancy and relevance optimization in orthogonal regression (GRROOR), is proposed.
The optimization problem is formulated as a constrained Non-negative Matrix Factorization (NMF) problem, and an algorithm is presented to efficiently find the ...
A new multi-label feature selection algorithm that effectively resolves existing algorithms' issues through three innovative procedures and can effectively ...